Major changes have taken place in the financial industry over the last century. The exponential growth of technology has introduced us to a new “world”. In this world, the sky is the limit and humanity could never imagine the discoveries made so far. Digital technologies have been affected by overall technological progress too. Admittedly this growth has resulted in an increased rate of data production, thus we found new ways to handle all this demand for storage like Digital Clouds. The immense data formation which can be analyzed for revealing trends, patterns, or statistical correlations associated with human behaviors, is defined as “Big Data”. Let’s dive into the “Big data journey”.
Additionally, there are three main types of data dependent on their structure form; Structured, Unstructured, and Semi-structure. Nowadays, industries – companies are highly interested in storing valuable data which can be leveraged for their own advantage. They can use them to extract worth-exploited information with techniques like Data mining. Therefore, over the last few years, a field has been created to cover the need for data leveraging… the so-called Data Science. Despite the fact that this task is complicated, in most cases, it can be delivered by automation tools like Machine Learning (ML) or Artificial Intelligence (AI) technologies.
Big data are differentiated by five main characteristics which are called the ‘5 Vs’:
1. The Volume
Volume refers to the amount of information produced. A caveat of big volumes is quality control (value of data). For example, huge companies like Facebook have billions of users… imagine the humongous quantity of information that is delivered in any given period of time.
2. The Velocity of the information is created, gathered, and processed.
It is very important for companies to be able to store data in real-time because if they don’t, they will be forced to manage all this intelligence afterward and that could result in obtaining useless or outdated data. A very good implementation of quick or even real-time data processing applies to digital advertising.
3. The Variety
Identifying different types of information among the data is crucial if there is a need for a particular analysis. For example, a text or an image sent to a friend is included in different categories of intelligence. Thus, the algorithm should be built to recognize the differences and act accordingly.
4. The Veracity of information
Veracity is referring to the accuracy and trustworthiness of big data. The challenging part is not the data itself but its origins. The information has to be cleared up from Bias, replications, and abnormalities to be considered actually reliable. Unfortunately, absolute “clearance” is almost impossible to occur but it is succeeded at a very good level.
5. The Value
Getting the insights is an important step but random information means nothing and has no value. Admittedly the conversion of useless data to valuable data is very challenging but it is essential.
Such technologies are implemented widely and it has reinforced the dynamic of growth in organizations. The future of the field is not easy to predict, but it has started to become more specialized. Being part of Data science demands a minimum of academic qualifications. More and more fields are getting involved with it and we have to be really thoughtful about the way we use it. The importance of big data though is highly appreciated. The big data “journey” has just started.
